Hauschild A-C, Baumbach J I, Baumbach J
Department Microfluidics and Clinical Diagnostics, KIST Europe, Saarbrücken, Germany.
Genet Mol Res. 2012 Aug 16;11(3):2733-44. doi: 10.4238/2012.July.10.17.
Exhaled air carries information on human health status. Ion mobility spectrometers combined with a multi-capillary column (MCC/IMS) is a well-known technology for detecting volatile organic compounds (VOCs) within human breath. This technique is relatively inexpensive, robust and easy to use in every day practice. However, the potential of this methodology depends on successful application of computational approaches for finding relevant VOCs and classification of patients into disease-specific profile groups based on the detected VOCs. We developed an integrated state-of-the-art system using sophisticated statistical learning techniques for VOC-based feature selection and supervised classification into patient groups. We analyzed breath data from 84 volunteers, each of them either suffering from chronic obstructive pulmonary disease (COPD), or both COPD and bronchial carcinoma (COPD + BC), as well as from 35 healthy volunteers, comprising a control group (CG). We standardized and integrated several statistical learning methods to provide a broad overview of their potential for distinguishing the patient groups. We found that there is strong potential for separating MCC/IMS chromatograms of healthy controls and COPD patients (best accuracy COPD vs CG: 94%). However, further examination of the impact of bronchial carcinoma on COPD/no-COPD classification performance is necessary (best accuracy CG vs COPD vs COPD + BC: 79%). We also extracted 20 high-scoring VOCs that allowed differentiating COPD patients from healthy controls. We conclude that these statistical learning methods have a generally high accuracy when applied to well-structured, medical MCC/IMS data.
呼出的气体携带着有关人体健康状况的信息。离子迁移谱仪与多毛细管柱（MCC/IMS）相结合是一种用于检测人体呼出气体中挥发性有机化合物（VOCs）的知名技术。该技术相对便宜、耐用且易于在日常实践中使用。然而，这种方法的潜力取决于能否成功应用计算方法来找到相关的挥发性有机化合物，并根据检测到的挥发性有机化合物将患者分类到特定疾病的特征组中。我们开发了一个集成的先进系统，使用复杂的统计学习技术进行基于挥发性有机化合物的特征选择，并将患者监督分类到不同组中。我们分析了84名志愿者的呼吸数据，他们中有些人患有慢性阻塞性肺疾病（COPD），有些人同时患有慢性阻塞性肺疾病和支气管癌（COPD + BC），还有35名健康志愿者作为对照组（CG）。我们对几种统计学习方法进行了标准化和整合，以全面了解它们区分患者组的潜力。我们发现，区分健康对照组和慢性阻塞性肺疾病患者的MCC/IMS色谱图具有很大潜力（慢性阻塞性肺疾病与对照组的最佳准确率：94%）。然而，有必要进一步研究支气管癌对慢性阻塞性肺疾病/非慢性阻塞性肺疾病分类性能的影响（对照组与慢性阻塞性肺疾病与慢性阻塞性肺疾病 + 支气管癌的最佳准确率：79%）。我们还提取了20种高分挥发性有机化合物，这些化合物能够区分慢性阻塞性肺疾病患者和健康对照组。我们得出结论，当应用于结构良好的医学MCC/IMS数据时，这些统计学习方法通常具有较高的准确率。
